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The use of $\alpha$-tantalum in superconducting circuits has enabled a considerable improvement of the coherence time of transmon qubits. The standard approach to grow $\alpha$-tantalum thin films on silicon involves heating the substrate, which takes several hours per deposition and prevents the integration of this material with wafers containing temperature-sensitive components. We report a detailed experimental study of an alternative growth method of $\alpha$-tantalum on silicon, which is achieved at room temperature through the use of a niobium seed layer. Despite a substantially higher density of oxygen-rich grain boundaries in the films sputtered at room temperature, resonators made from these films are found to have state-of-the-art quality factors, comparable to resonators fabricated from tantalum grown at high temperature. This finding challenges previous assumptions about correlations between material properties and microwave loss of superconducting thin films, and opens a new avenue for the integration of tantalum into fabrication flows with limited thermal budget.

Future quantum networks will be composed of both terrestrial links for metropolitan and continent-scale connections and space-based links for global coverage and infrastructure resilience. However, the propagation of quantum signals through the atmosphere is severely impacted by the effects of turbulence. This is even more the case for entanglement-based quantum communication protocols requiring two free-space channels to be considered simultaneously. In this work, we assess the advantage of turbulence mitigation by adaptive optics, in particular during daytime link operation, so as to increase the coupling of the received signal into an optical fiber. We show in particular that this improves the performance of entanglement-based quantum key distribution by up to a few hundred bits per second when compared with the uncorrected scenario

In this paper, we explore the quantization of K\"ahler manifolds, focusing on the relationship between deformation quantization and geometric quantization. We provide a classification of degree 1 formal quantizable functions in the Berezin-Toeplitz deformation quantization, establishing that these formal functions are of the form $f = f_0 - \frac{\hbar}{4\pi}(\Delta f_0 + c)$ for a certain smooth (non-formal) function $f_0$. If $f_0$ is real-valued then $f_0$ corresponds to a Hamiltonian Killing vector field. In the presence of Hamiltonian $G$-symmetry, we address the compatibility between the infinitesimal symmetry for deformation quantization via quantum moment map and infinitesimal symmetry on geometric quantization acting on Hilbert spaces of holomorphic sections via Berezin-Toeplitz quantization.

3d mirror symmetry is a mysterious duality for certian pairs of hyperk\"ahler manifolds, or more generally complex symplectic manifolds/stacks. In this paper, we will describe its relationships with 2d mirror symmetry. This could be regarded as a 3d analog of the paper "Mirror Symmetry is T-Duality" by Strominger, Yau and Zaslow which described 2d mirror symmetry via 1d dualities.

Despite constituting a smaller fraction of the qubits electromagnetic mode, surfaces and interfaces can exert significant influence as sources of high-loss tangents, which brings forward the need to reveal properties of these extended defects and identify routes to their control. Here, we examine the structure and composition of the metal-substrate interfacial layer that exists in Ta/sapphire-based superconducting films. Synchrotron-based X-ray reflectivity measurements of Ta films, commonly used in these qubits, reveal an unexplored interface layer at the metal-substrate interface. Scanning transmission electron microscopy and core-level electron energy loss spectroscopy identified an approximately 0.65 \ \text{nm} \pm 0.05 \ \text{nm} thick intermixing layer at the metal-substrate interface containing Al, O, and Ta atoms. Density functional theory (DFT) modeling reveals that the structure and properties of the Ta/sapphire heterojunctions are determined by the oxygen content on the sapphire surface prior to Ta deposition, as discussed for the limiting cases of Ta films on the O-rich versus Al-rich Al2O3 (0001) surface. By using a multimodal approach, integrating various material characterization techniques and DFT modeling, we have gained deeper insights into the interface layer between the metal and substrate. This intermixing at the metal-substrate interface influences their thermodynamic stability and electronic behavior, which may affect qubit performance.

This article introduces an innovative architecture designed to declaratively combine Large Language Models (LLMs) with shared histories, and triggers to identify the most appropriate LLM for a given task. Our approach is general and declarative, relying on the construction of finite automata coupled with an event management system. The developed tool is crafted to facilitate the efficient and complex integration of LLMs with minimal programming effort, especially, but not only, for integrating methods of positive psychology to AI. The flexibility of our technique is demonstrated through applied examples in automation, communication, and ethics., Comment: Submitted to IAAI-2025, Philadelphia, PA

In the SYZ program, the mirror of $Y$ is the moduli space of Lagrangian branes in $Y$. When $Y$ is equipped with a Hamiltonian $G$-action, we prove that its mirror determines a canonical complex Lagrangian subvariety in the Coulomb branch of the 3d $\mathcal{N}=4$ pure $G$-gauge theory., Comment: This version includes updates to several references

The integration of Large Language Models (LLMs) like GPT-4o into robotic systems represents a significant advancement in embodied artificial intelligence. These models can process multi-modal prompts, enabling them to generate more context-aware responses. However, this integration is not without challenges. One of the primary concerns is the potential security risks associated with using LLMs in robotic navigation tasks. These tasks require precise and reliable responses to ensure safe and effective operation. Multi-modal prompts, while enhancing the robot's understanding, also introduce complexities that can be exploited maliciously. For instance, adversarial inputs designed to mislead the model can lead to incorrect or dangerous navigational decisions. This study investigates the impact of prompt injections on mobile robot performance in LLM-integrated systems and explores secure prompt strategies to mitigate these risks. Our findings demonstrate a substantial overall improvement of approximately 30.8% in both attack detection and system performance with the implementation of robust defence mechanisms, highlighting their critical role in enhancing security and reliability in mission-oriented tasks.

The calibration of predictive distributions has been widely studied in deep learning, but the same cannot be said about the more specific epistemic uncertainty as produced by Deep Ensembles, Bayesian Deep Networks, or Evidential Deep Networks. Although measurable, this form of uncertainty is difficult to calibrate on an objective basis as it depends on the prior for which a variety of choices exist. Nevertheless, epistemic uncertainty must in all cases satisfy two formal requirements: first, it must decrease when the training dataset gets larger and, second, it must increase when the model expressiveness grows. Despite these expectations, our experimental study shows that on several reference datasets and models, measures of epistemic uncertainty violate these requirements, sometimes presenting trends completely opposite to those expected. These paradoxes between expectation and reality raise the question of the true utility of epistemic uncertainty as estimated by these models. A formal argument suggests that this disagreement is due to a poor approximation of the posterior distribution rather than to a flaw in the measure itself. Based on this observation, we propose a regularization function for deep ensembles, called conflictual loss in line with the above requirements. We emphasize its strengths by showing experimentally that it restores both requirements of epistemic uncertainty, without sacrificing either the performance or the calibration of the deep ensembles.

This study investigates the relationship between automatic information extraction descriptors and manual analyses to describe gender representation disparities in TV and Radio. Automatic descriptors, including speech time, facial categorization and speech transcriptions are compared with channel reports on a vast 32,000-hour corpus of French broadcasts from 2023. Findings reveal systemic gender imbalances, with women underrepresented compared to men across all descriptors. Notably, manual channel reports show higher women's presence than automatic estimates and references to women are lower than their speech time. Descriptors share common dynamics during high and low audiences, war coverage, or private versus public channels. While women are more visible than audible in French TV, this trend is inverted in news with unseen journalists depicting male protagonists. A statistical test shows 3 main effects influencing references to women: program category, channel and speaker gender., Comment: keywords : Gender representation, computational humanities, TV, Radio, face classification, speaker traits, ASR, media, SLU. Accepted to InterSpeech 2024, Kos Island, Greece, september 2024

In 1971 C.F.\ Miller associated to every finitely presented group $G$ a free-by-free group $M(G)$ known as the Miller Machine, whose conjugacy problem is closely related to the conjugacy and word problems of $G$. We quantify this relationship, and look to fully understand the conjugacy problem of $M(G)$; namely, we reduce the conjugacy problem in $M(G)$ to a strong form of list conjugacy in $G$, which we term iso-computational list conjugacy. As an application, we show that if $G$ is finite, the conjugacy problem for $M(G)$ is in $\mathsf{PSPACE}$., Comment: Published in International Journal of Algebra and Computation

In this paper, we study the Floer theory of equivariant Lagrangian correspondences and apply it to deduce a conjecture of Teleman, which finds the relation between the disc potential of an invariant Lagrangian submanifold and that of its quotient. A main step is to extend Fukaya's construction of an $A_\infty$ tri-module for Lagrangian correspondences to Borel spaces. We find that the equivariant obstruction of a Lagrangian correspondence plays an essential role, which leads to quantum corrections in the disc potentials of quotients. We solve the obstruction in the toric setup and find the relation with mirror maps for compact semi-Fano toric manifolds., Comment: Exposition improved

Since smart cities aim at becoming self-monitoring and self-response systems, their deployment relies on close resource monitoring through large-scale urban sensing. The subsequent gathering of massive amounts of data makes essential the development of event-filtering mechanisms that enable the selection of what is relevant and trustworthy. Due to the rise of mobile event producers, location information has become a valuable filtering criterion, as it not only offers extra information on the described event, but also enhances trust in the producer. Implementing mechanisms that validate the quality of location information becomes then imperative. The lack of such strategies in cloud architectures compels the adoption of new communication schemes for Internet of Things (IoT)-based urban services. To serve the demand for location verification in urban event-based systems (DEBS), we have designed three different fog architectures that combine proximity and cloud communication. We have used network simulations with realistic urban traces to prove that the three of them can correctly identify between 73% and 100% of false location claims.

Electrochemical degradation of solid electrolytes is a major roadblock in the development of solid-state batteries, and the formed solid-solid interphase (SSI) plays a key role in the performance of solid-state batteries. In this study, by combining experimental X-ray absorption spectroscopy (XAS) measurements, first-principles simulations, and unsupervised machine learning, we have unraveled the atomic-scale oxidative degradation mechanisms of sulfide electrolytes at the interface using the baseline Li3PS4 (LPS) electrolyte as a model system. The degradation begins with a decrease of Li neighbor affinity to S atoms upon initial delithiation, followed by the formation of S-S bonds as the PS4 tetrahedron deforms. After the first delithiation cycle, the PS4 motifs become strongly distorted and PS3 motifs start to form. Spectral fingerprints of the local structural evolution are identified, which correspond to the main peak broadening and the peak shifting to a higher energy by about 2.5 eV in P K-edge XAS and a new peak emerging at 2473 eV in S K-edge XAS during delithiation. The spectral fingerprints serve as a proxy for the electrochemical stability of phosphorus sulfide solid electrolytes beyond LPS, as demonstrated in argyrodite Li6PS5Cl. We observed that the strong distortion and destruction of PS4 tetrahedra and the formation of S-S bonds are correlated with an increased interfacial impedance. To the best of our knowledge, this study showcases the first atomic-scale insights into the oxidative degradation mechanism of the LPS electrolyte, which can provide guidance for controlling macroscopic reactions through microstructural engineering and, more generally, can advance the rational design of sulfide electrolytes.

Scaling up superconducting quantum circuits based on transmon qubits necessitates substantial enhancements in qubit coherence time. Among the materials considered for transmon qubits, tantalum (Ta) has emerged as a promising candidate, surpassing conventional counterparts in terms of coherence time. However, the presence of an amorphous surface Ta oxide layer introduces dielectric loss, ultimately placing a limit on the coherence time. In this study, we present a novel approach for suppressing the formation of tantalum oxide using an ultrathin magnesium (Mg) capping layer deposited on top of tantalum. Synchrotron-based X-ray photoelectron spectroscopy (XPS) studies demonstrate that oxide is confined to an extremely thin region directly beneath the Mg/Ta interface. Additionally, we demonstrate that the superconducting properties of thin Ta films are improved following the Mg capping, exhibiting sharper and higher-temperature transitions to superconductive and magnetically ordered states. Based on the experimental data and computational modeling, we establish an atomic-scale mechanistic understanding of the role of the capping layer in protecting Ta from oxidation. This work provides valuable insights into the formation mechanism and functionality of surface tantalum oxide, as well as a new materials design principle with the potential to reduce dielectric loss in superconducting quantum materials. Ultimately, our findings pave the way for the realization of large-scale, high-performance quantum computing systems.

In this paper, we construct and classify a new family of flips, called generalized Grassmannian flips, by generalizing the construction of standard flips for $\mathbb{P}^m\times \mathbb{P}^n$ to any generalized Grassmannian $G/P$, where $P$ is a maximal parabolic subgroup of a complex semi-simple algebraic group. In addition, we show that a 9-fold generalized Grassmannian flip for $Sp(6, \mathbb{C})$ satisfies the DK flip conjecture by Bondal-Orlov and Kawamata via mutation techniques by Kuznetsov and Thomas' chess game method., Comment: To appear in Mathematical Research Letters

Nitrogen-vacancy centers in diamond are a promising platform for nanoscale nuclear magnetic resonance sensing. Despite significant progress towards using NV centers to detect and localize nuclear spins down to the single spin level, NV-based spectroscopy of individual, intact, arbitrary target molecules remains elusive. NV molecular sensing requires that target molecules are immobilized within a few nanometers of NV centers with long spin coherence time. The inert nature of diamond typically requires harsh functionalization techniques such as thermal annealing or plasma processing, limiting the scope of functional groups that can be attached to the surface. Solution-phase chemical methods can be more readily generalized to install diverse functional groups, but they have not been widely explored for single-crystal diamond surfaces. Moreover, realizing shallow NV centers with long spin coherence times requires highly ordered single-crystal surfaces, and solution-phase functionalization has not yet been shown to be compatible with such demanding conditions. In this work, we report a versatile strategy to directly functionalize C-H bonds on single-crystal diamond surfaces under ambient conditions using visible light. This functionalization method is compatible with charge stable NV centers within 10 nm of the surface with spin coherence times comparable to the state of the art. As a proof of principle, we use shallow ensembles of NV centers to detect nuclear spins from functional groups attached to the surface. Our approach to surface functionalization based on visible light-driven C-H bond activation opens the door to deploying NV centers as a broad tool for chemical sensing and single-molecule spectroscopy.

Cryptocurrency market is known for exhibiting significantly higher volatility than traditional asset classes. Efficient and adequate risk calculation is vital for managing risk exposures in such market environments where extreme price fluctuations occur in short timeframes. The objective of this thesis is to build a real-time computation workflow that provides VaR estimates for non-linear portfolios of cryptocurrency derivatives. Many researchers have examined the predictive capabilities of time-series models within the context of cryptocurrencies. In this work, we applied three commonly used models - EMWA, GARCH and HAR - to capture and forecast volatility dynamics, in conjunction with delta-gamma-theta approach and Cornish-Fisher expansion to crypto derivatives, examining their performance from the perspectives of calculation efficiency and accuracy. We present a calculation workflow which harnesses the information embedded in high-frequency market data and the computation simplicity inherent in analytical estimation procedures. This workflow yields reasonably robust VaR estimates with calculation latencies on the order of milliseconds.

A compact K\"ahler manifold $\left( M,\omega ,J\right) $ with $T$-symmetry admits a natural mixed polarization $\mathcal{P}_{\mathrm{mix}}$ whose real directions come from the $T$-action. In \cite{LW1}, we constructed a one-parameter family of K\"ahler structures $\left( \omega ,J_{t}\right) $'s with the same underlying K\"a hler form $\omega $ and $J_{0}=J$, such that (i) there is a $T$-equivariant biholomorphism between $\left( M,J_{0}\right) $ and $\left( M,J_{t}\right) $ and (ii) K\"ahler polarizations $\mathcal{P} _{t}$'s corresponding to $J_{t}$'s converge to $\mathcal{P}_{\mathrm{mix}}$ as $t$ goes to infinity. In this paper, we study the quantum analog of above results. Assume $L$ is a pre-quantum line bundle on $\left( M,\omega \right) $. Let $\mathcal{H}_{t}$ and $ \mathcal{H}_{\mathrm{mix}}$ be quantum spaces defined using polarizations $\mathcal{P}_{t}$ and $\mathcal{P}_{\mathrm{mix}}$ respectively. In particular, $\mathcal{H}_{t}=H_{\bar{\partial}_{t}}^{0}\left( M,L\right) $. They are both representations of $T$. We show that (i) there is a $T$-equivariant isomorphism between $\mathcal{H}_{0}$ and $\mathcal{H}_{\mathrm{mix}}$ and (ii) for regular $T$-weight $\lambda $, corresponding $\lambda $-weight spaces $ \mathcal{H}_{t,\lambda }$'s converge to $\mathcal{H}_{\mathrm{mix},\lambda }$ as $t$ goes to infinity., Comment: Minor changes based on referee's suggestions. To appear in Proc. Lond. Math. Soc

Clustering methods are being applied to a wider range of scenarios involving more complex datasets, where the shapes of clusters tend to be arbitrary. In this paper, we propose a novel Path-based Valley-seeking clustering algorithm for arbitrarily shaped clusters. This work aims to seek the valleys among clusters and then individually extract clusters. Three vital techniques are used in this algorithm. First, path distance (minmax distance) is employed to transform the irregular boundaries among clusters, that is density valleys, into perfect spherical shells. Second, a suitable density measurement, $k$-distance, is employed to make adjustment on Minimum Spanning Tree, by which a robust minmax distance is calculated. Third, we seek the transformed density valleys by determining their centers and radius. First, the clusters are wrapped in spherical shells after the distance transformation, making the extraction process efficient even with clusters of arbitrary shape. Second, adjusted Minimum Spanning Tree enhances the robustness of minmax distance under different kinds of noise. Last, the number of clusters does not need to be inputted or decided manually due to the individual extraction process. After applying the proposed algorithm to several commonly used synthetic datasets, the results indicate that the Path-based Valley-seeking algorithm is accurate and efficient. The algorithm is based on the dissimilarity of objects, so it can be applied to a wide range of fields. Its performance on real-world datasets illustrates its versatility.

For a fixed prequantum line bundle $L$ over a hyperK\"ahler manifold $X$, we find a natural $\operatorname{Sp}(1)$-action on $\Omega^*(X, L)$ intertwining a twistor family of $\operatorname{Spin}^{\operatorname{c}}$-Dirac Laplacians on the spaces of $L$-valued $(0, *)$-forms on $X$, noting that $L$ is holomorphic for only one complex structure in the twistor family. This establishes a geometric quantization of $X$ via Gukov-Witten brane quantization and leads to a proposal of a mathematical definition of $\operatorname{Hom}(\overline{\mathcal{B}}_{\operatorname{cc}}, \mathcal{B}_{\operatorname{cc}})$ for the canonical coisotropic A-brane $\mathcal{B}_{\operatorname{cc}}$ on $X$ and its conjugate brane $\overline{\mathcal{B}}_{\operatorname{cc}}$., Comment: 19 pages

A major roadblock to scalable quantum computing is phase decoherence and energy relaxation caused by qubits interacting with defect-related two-level systems (TLS). Native oxides present on the surfaces of superconducting metals used in quantum devices are acknowledged to be a source of TLS that decrease qubit coherence times. Reducing microwave loss by surface engineering (i.e., replacing uncontrolled native oxide of superconducting metals with a thin, stable surface with predictable characteristics) can be a key enabler for pushing performance forward with devices of higher quality factor. In this work, we present a novel approach to replace the native oxide of niobium (typically formed in an uncontrolled fashion when its pristine surface is exposed to air) with an engineered oxide, using a room-temperature process that leverages Accelerated Neutral Atom Beam (ANAB) technology at 300 mm wafer scale. This ANAB beam is composed of a mixture of argon and oxygen, with tunable energy per atom, which is rastered across the wafer surface. The ANAB-engineered Nb-oxide thickness was found to vary from 2 nm to 6 nm depending on ANAB process parameters. Modeling of variable-energy XPS data confirm thickness and compositional control of the Nb surface oxide by the ANAB process. These results correlate well with those from transmission electron microscopy and X-ray reflectometry. Since ANAB is broadly applicable to material surfaces, the present study indicates its promise for modification of the surfaces of superconducting quantum circuits to achieve longer coherence times., Comment: 22 pages, 7 figures, will be submitted to Superconductor Science and Technology Special Focus Issue Journal